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Python Rapid
Artificial Intelligence
Ab Initio Molecular Dynamics
Author @Jingbai Li
2022 – present Hoffmann Institute of Advanced Materials
Shenzhen Polytechnic, Shenzhen, China
2019 – 2022 Department of Chemistry and Chemical Biology
Northeastern University, Boston, USA
version: 2.4.0
With contribution from (in alphabetic order):
Jingbai Li - Fewest switches surface hopping
Zhu-Nakamura surface hopping
Velocity Verlet
OpenMolcas interface
OpenMolcas/Tinker interface
BAGEL interface
ORCA interface
GFN-xTB interface
Adaptive sampling
Grid search
Two-layer ONIOM
Periodic boundary condition (coming soon)
Wall potential
QC/ML hybrid NAMD
Excited-state Equivariant Neural Network
Patrick Reiser - Fully connected neural networks (pyNNsMD)
SchNet (pyNNsMD)
Special acknowledgement to:
Steven A. Lopez - Project co-founder
Pascal Friederich - Project co-founder
- Machine learning nonadiabatic molecular dynamics (ML-NAMD).
- Neural network training and grid search.
- Active learning with ML-NAMD trajectories.
- Support BAGEL, Molcas, ORCA, OpenQP, GFN-xTB for QM, and Molcas/Tinker for QM/MM calculations.
- Generalized FSSH and ZNSH with nonadiabatic coupling and spin-orbit coupling
- Add curvature-driven time-dependent coupling for FSSH
- Support QMQM2 scheme multiscale dynamics
- Python >=3.7 PyRAI2MD is written and tested in Python 3.7.4. Older version of Python is not tested and might not be working properly.
- TensorFlow >=2.2 TensorFlow/Keras API is required to load the trained NN models and predict energy and force.
- Cython PyRAI2MD uses Cython library for efficient surface hopping calculation.
- Matplotlib/Numpy Scientific graphing and numerical library for plotting training statistic and array manipulation.
- scikit-learn Machine learning library
File/Folder Name Description
---------------------------------------------------------------------------------------------------
PyRAI2MD source codes folder
|--pyrai2md.py PyRAI2MD main function
|--method.py PyRAI2MD method manager
|--variables.py PyRAI2MD input reader
|
|--Keywords default input values folder
| |--key_control.py keywords for calculation control
| |--key_molecule.py keywords for molecule specification
| |--key_md.py keywords for molecular dynamic settings
| |--key_nn.py keywords for neural network settings
| |--key_grid_search.py keywords for grid search settings
| |--key_molcas.py keywords for molcas calculation
| |--key_bagel.py keywords for bagel calculation
| |--key_orca keywords for orca calculation
| |--key_openqp keywords for openqp calculation
| |--key_xtb.py keywords for xtb calculation
| |--key_mlp.py keywords for mlp settings
| |--key_schnet.py keywords for schnet settings
| |--key_e2n2.py keywords for e2n2 settings
| |--key_dimenet.py keywords for dimenet (NAC model) setting
| |--key_read_file.py keywords for reading training data
| `-key_templ.py keywords class template
|
|--Molecule atom, molecule, trajectory code folder
| |--atom.py atomic properties class
| |--molecule.py molecular properties class
| |--trajectory.py trajectory properties class
| |--constraint.py external potential functions
| |--pbc_helper.py periodic boundary condition functions
| `-qmmm_helper.py qmmm functions
|
|--Quantum_Chemistry quantum chemicial program interface folder
| |--qc_molcas.py OpenMolcas interface
| |--qc_bagel.py BAGEL interface
| |--qc_molcas_tinker OpenMolcas/Tinker interface
| |--qc_orca ORCA interface
| |--qc_openQP OpenQP interface
| |--qc_xtb GFN-xTB interface
| `-qmqm2 Multiscale calculation interface
|
|--Machine_Learning machine learning library interface folder
| |--model_demo.py demo version neural network
| |--model_NN.py native neural network interface
| |--model_pyNNsMD.py pyNNsMD interface
| |--model_GCNNP.py GCNNP interface
| |--model_DimeNet.py DimeNet NAC model interface
| |--model_templ.py NN interface template
| |--model_helper.py additional tools for neural network
| |--hyper_nn.py native neural network hyperparameter
| |--hyper_pynnsmd.py pyNNsMD hyperparameter
| |--hyper_gcnnp.py GCNNP hyperparameter
| |--hyper_dimenet.py DimeNet NAC model hyperparameter
| |--hyper_templ.py hyperparameter template
| |--training_data.py training data manager
| |--permutation.py data permutation functions
| |--adaptive_sampling.py adaptive sampling class
| |--grid_search.py grid search manager
| |--search_nn.py grid search function for native nn
| |--search_GCNNP.py grid search function for e2n2
| |--remote_train.py remote training function
| |--Dimenet.py Dimenet NAC model
| |--NNsMD demo version neural network library
| `-pyNNsMD native neural network library
|
|--Dynamics ab initio molecular dynamics code folder
| |--aimd.py molecular dynamics class
| |--mixaimd.py ML-QC hybrid molecular dynamics class
| |--single_point.py single point calculation
| |--hop_probability.py surface hopping probability calculation
| |--reset_velocity.py velocity adjustment functions
| |--verlet.py velocity verlet method
| |--Ensembles thermodynamics control code folder
| | |--ensemble.py thermodynamics ensemble manager
| | |--microcanonical.py microcanonical ensemble
| | `-thermostat.py canonical ensemble
| |
| `-Propagators electronic propagation code folder
| |--surface_hopping.py surface hopping manager
| |--setup_fssh.py setup file to compile the C-lib of fssh.pyx
| |--fssh.pyx fewest switches surface hopping method
| |--gsh.py generalized surface hopping method
| `-tsh_helper.py trajectory surface hopping tools
|
`-Utils utility folder
|--extension.py additional tools for setup
|--coordinates.py coordinates writing functions
|--read_tools.py index reader
|--geom_tools.py compute geometrical derivatives
|--bonds.py bond length library
|--sampling.py initial condition sampling functions
|--timing.py timing functions
`-logo.py logo and credits
Download the repository
git clone https://github.com/mlcclab/PyRAI2MD-hiam.git
Install the codes, this will create a command pyrai2md to run calculations
cd ./PyRAI2MD-hiam
pip install .
Compile fssh library using pyrai2md command
pyrai2md update
Go to the test folder
cd ./test
Choose test jobs by editing test_case.py, set test_job = 1 to turn on the test or 0 to turn off the test
nano test_case.py
Modify the environment variables in the template
nano run_test.sh
Run test, this might take a while
bash run_test.sh
pyrai2md input
We are currently working on the tutorials for users manual.
- Jingbai Li, Patrick Reiser, Benjamin R. Boswell, André Eberhard, Noah Z. Burns, Pascal Friederich, and Steven A. Lopez, "Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations", Chem. Sci. 2021, 12, 5302-5314. DOI:10.1039/D0SC05610C
- Jingbai Li, Rachel Stein, Daniel Adrion, Steven A. Lopez, "Machine-learning photodynamics simulations uncover the role of substituent effects on the photochemical formation of cubanes", J. Am. Chem. Soc. 2021, 143, 48, 20166–20175. DOI:10.1021/jacs.1c07725
- Jingbai Li, Steven A. Lopez, “Excited-state distortions promote the reactivities and regioselectivities of photochemical 4π-electrocyclizations of fluorobenzenes”, Chem. A Eur J. 2022, 28, e202200651. DOI:10.1002/chem.202200651
- Jingbai Li, Steven A. Lopez, “A Look Inside the Black Box of Machine Learning Photodynamics Simulations”, Acc. Chem. Res., 2022, 55, 1972–1984. DOI:10.1021/acs.accounts.2c00288
- L. Wang, C. Salguero, S.A. Lopez, J. Li, "Machine learning photodynamics uncover blocked non-radiative mechanisms in aggregation-induced emission", Chem, 2024, 10, 2295–2310. DOI: 10.1016/j.chempr.2024.04.017